论文标题
在个性化治疗效果估计中的对抗性脱节
Adversarial De-confounding in Individualised Treatment Effects Estimation
论文作者
论文摘要
由于越来越多的非实验性观察数据以及实验研究的局限性，例如相当大的成本，不切实际性，小且较低的代表性样本量等，观察性研究最近受到了机器学习社区的极大关注。在观察性研究中，DENOCONSENTING是一个基本的治疗效应（ITE）估计的基本问题。本文提出了通过对抗训练的分离表示，以选择性地平衡二元处理设置中的混杂因素，以进行ITE估计。对治疗政策的对抗性培训有选择地鼓励混杂因素的治疗敏捷平衡表示，并通过反事实推断有助于估计观察性研究中的ITE。关于合成和现实世界数据集的经验结果，具有不同程度的混淆，证明我们提出的方法可以改善最新方法，从而在ITE估计中实现较低的误差。
Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.
